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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Phonon Olympics: Phonon property and lattice thermal conductivity benchmarking from open-source packages

Three widely used open-source packages for determining phonon properties and lattice thermal conductivities (ALAMODE, phono3py, and ShengBTE) are benchmarked by teams of expert users and the package developers. The phonons for Ge, RbBr, monolayer MoSe 2 , and AlN are modeled at zero temperature, and they scatter through three-phonon and phonon-isotope processes, with thermal conductivities obtained from the linearized Peierls–Boltzmann transport equation with input from density functional theory calculations. Over a wide range of temperatures, the thermal conductivities calculated by the teams fall within at most ±15% of their mean values for each of the four materials. The phonon frequencies, obtained from the harmonic force constants, do not show large differences between the calculations, indicating that the modal heat capacities and group velocities are not responsible for the thermal conductivity variations. It is the lifetimes associated with three-phonon scattering, obtained from the cubic force constants, that drive the variations. The many decisions required to calculate the cubic force constants (e.g., supercell size, atomic displacement, neighbor cutoff, and application of symmetries) make identification of the precise origin of the thermal conductivity variations challenging. The calculated thermal conductivities do not generally show agreement with experimental measurements, which is attributed to the limitations of the density functional theory calculations. Guidance for the development of best practices is provided, which will help to standardize protocols needed for building thermal conductivity databases. The results provide a baseline for future benchmarking of other packages and more advanced calculations.

McGaughey, Alan J. H. [Carnegie Mellon Univ., Pitt↗
DOE OSTI2022

System identity and transformation in petroleum jurisdictions: A multi-method approach for the North Slope Borough, Alaska

Capturing the multidimensionality of a bounded social-environmental system (SES) presents a range of challenges to interdisciplinary researchers due to the need to integrate divergent scientific paradigms, scalar data, and social theories. Contemporary Arctic circumpolar SESs studied under conditions of rapid and unprecedented climatic, ecological, economic, and sociopolitical change, defy any singular established methodological approach that aims to schematize and interpret the system for decision-making purposes. As a small interdisciplinary team working within a large Arctic SES modeling effort, we have found that developing systems models to support resilience in the Arctic requires an understanding of system dynamics that is attentive to holistic indicators of change, measured both quantitatively and qualitatively. Using the Alaska North Slope Borough as a case study, we apply three convergent frameworks to capture significant dimensions of the system for improved problem definition in confronting the challenges of Arctic climate change. We describe contemporary “oil and gas” social-ecological system components and dynamics, the historical processes and transformations that fundamentally altered the system, and the scientific projections for the most likely catalysts of future change. This analysis results in a typology for defining subnational Arctic hydrocarbon SESs. We conclude that the future of oil and gas development as a policy pathway in different locations experiencing rapid climate change can be evaluated when difficult-to-quantify variables are included.

Lovecraft, Amy Lauren↗
DOE OSTI2024

Optimal experimental design: Formulations and computations

Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of Two-Step Method for Fast Chloride MSR Neutronics

This work presents neutronics models of a small and large fast-spectrum molten chloride-salt reactor. The models are similar to designs being pursued by industry, and they may serve as generic preconceptual and simplified neutronics models that provide information for decision making in licensing-related areas. Here, the two models were created using Serpent, a Monte Carlo neutron transport code, and Moltres, a neutron diffusion core simulator tool. Specifically, this study focused on exploring the applicability of diffusion theory to fast molten salt reactor (MSR) models, the capabilities of an open-source, MSR-oriented simulation tool (Moltres), and optimal energy-group structures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets

With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

active distribution network↗
DOE OSTI2021

Decoupling between Shockley partials and stacking faults strengthens multiprincipal element alloys

We report that mechanical properties are fundamental to structural materials, where dislocations play a decisive role in describing their mechanical behavior. Although the high-yield stresses of multiprincipal element alloys (MPEAs) have received extensive attention in the last decade, the relation between their mechanistic origins remains elusive. Our multiscale study of density functional theory, atomistic simulations, and high-resolution microscopy shows that the excellent mechanical properties of MPEAs have diverse origins. The strengthening effects through Shockley partials and stacking faults can be decoupled in MPEAs, breaking the conventional wisdom that low stacking fault energies are coupled with wide partial dislocations. This study clarifies the mechanistic origins for the strengthening effects, laying the foundation for physics-informed predictive models for materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Role of orbital selectivity on crystal structures and electronic states in BiMnO 3 and LaMnO 3 perovskites

Correlated oxides, such as BiMnO 3 and LaMnO 3 , show a complex interplay of electronic correlations and crystal structure exhibiting multiple first-order phase transitions, some without a clear order parameter. The quantitative theoretical description of this temperature-dependent electronic-structural interplay in the vicinity of a Mott transition is still a challenge. Here we address this issue by simultaneously considering both structural and electronic degrees of freedom, within a self-consistent density functional theory with embedded dynamical mean field theory. Our results show the existence of novel electronic states characterized by the coexistence of insulating, semimetallic, and metallic orbitals. This state is in agreement with resonant x-ray scattering. Here we also show that electronic entropy plays a decisive role in both electronic and structural phase transitions. By self-consistent determination of both the electronic state and the corresponding crystal structure, we show that the temperature evolution of these phases can be quantitatively explained from first principles, thus demonstrating the predictive power of the theoretical method for both the structural and the electronic properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Accelerating the development of new solar absorbers by photoemission characterization coupled with density functional theory

Abstract The expectation to progress towards Terawatts production by solar technologies requires continuous development of new materials to improve efficiency and lower the cost of devices beyond what is currently available at industrial level. At the same time, the turnaround time to make the investment worthwhile is progressively shrinking. Whereas traditional absorbers have developed in a timeframe spanning decades, there is an expectation that emerging materials will be converted into industrially relevant reality in a much shorter timeframe. Thus, it becomes necessary to develop new approaches and techniques that could accelerate decision-making steps on whether further research on a material is worth pursuing or not. In this review, we will provide an overview of the photoemission characterization methods and theoretical approaches that have been developed in the past decades to accelerate the transfer of emerging solar absorbers into efficient devices.

14 SOLAR ENERGY↗
DOE OSTI2025

Search for charged-lepton flavor violation in the production and decay of top quarks using trilepton final states in proton-proton collisions at $\sqrt{s}$ =13 TeV

A search is performed for charged-lepton flavor violating processes in top quark (𝑡) production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 138 fb −1 . The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between 0.024–0.424 TeV −2 depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, 𝑡 → 𝑒⁢𝜇⁢𝑢 (𝑡 → 𝑒⁢𝜇⁢𝑐), of 0.032⁢(0.498) × 10 −6 , 0.022⁢(0.369) × 10 −6 , and 0.012⁢(0.216) × 10 −6 for tensorlike, vectorlike, and scalarlike interactions, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results—especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes’ rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated ‘engine’ capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Science & Technology - Other Topics↗
DOE OSTI2022

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

TeV-scale lepton number violation: Connecting leptogenesis, neutrinoless double beta decay, and colliders

In the context of TeV-scale lepton number violating (LNV) interactions, we illustrate the interplay between leptogenesis, neutrinoless double beta ( 0 ν β β ) decay, and LNV searches at proton-proton colliders. Using a concrete model for illustration, we overcome the limitations of previous effective field theory analyses and are able to identify the parameter space where standard thermal leptogenesis is rendered unviable due to washout processes. Moreover, we show how 0 ν β β decay and p p collisions provide complementary probes. We find that the new particle spectrum can have a decisive impact on the relative sensitivity of these two probes. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗
DOE OSTI2021

Learning to Unknot

We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots. We study the UNKNOT problem of determining whether or not a given knot is the unknot. After describing an algorithm to randomly generate N-crossing braids and their knot closures and discussing the induced prior on the distribution of knots, we apply binary classification to the UNKNOT decision problem. We find that the Reformer and shared-QK Transformer network architectures outperform fully-connected networks, though all perform at 95% accuracy. Perhaps surprisingly, we find that accuracy increases with the length of the braid word, and that the networks learn a direct correlation between the confidence of their predictions and the degree of the Jones polynomial. Finally, we utilize reinforcement learning (RL) to find sequences of Markov moves and braid relations that simplify knots and can identify unknots by explicitly giving the sequence of unknotting actions. Trust region policy optimization (TRPO) performs consistently well, reducing 80% of the unknots with up to 96 crossings we tested to the empty braid word, and thoroughly outperformed other RL algorithms and random walkers. Studying these actions, we find that braid relations are more useful in simplifying to the unknot than one of the Markov moves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
DOE OSTI2022

Experimental validation of multiphysics model simulations of the thermal response of a cement clinker rotary kiln at laboratory scale

Abstract An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world‐wide, producing a significant increase in related global energy demand. With approximately 7% of the world‐wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO 2 ), alumina (Al 2 O 3 ), and iron (Fe 2 O 3 ) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory‐scale rotary cement clinker kiln, and produced cement clinker at laboratory‐scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory‐scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH 4 ) gas and oxygen (O 2 ). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.

Tabares, Juan David↗
DOE OSTI2024

Center For Actinide Science and Technology (CAST)

CAST encompassed 17 senior researchers in its first funding cycle and added two additional theorists briefly in the final two years. Approximately 37 graduate students and post-doctoral associates were supported by CAST. Within the 19 senior researchers there were changes in composition since the center was first funded. One researcher left associated with a move to another university. Two were phased out because they did not meet the expectations of CAST in terms of research performance. These decisions were always made in consultation with our External Advisory Committee (EAC) and DOE management. In fact, the phasing out of the two PI’s was suggested first by the EAC. Two new researchers were also brought in response to feedback at our midterm review where it was suggested that we needed increased theory support in CAST. No further changes were implemented since then, and CAST researchers thought that stability and optimal productivity was achieved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗